Nearest Neighbour Few-Shot Learning for Cross-lingual Classification

• URL: http://arxiv.org/abs/2109.02221v1
• Date: Mon, 6 Sep 2021 03:18:23 GMT
• Title: Nearest Neighbour Few-Shot Learning for Cross-lingual Classification
• Authors: M Saiful Bari, Batool Haider, Saab Mansour
• Abstract summary: Cross-lingual adaptation using a simple nearest neighbor few-shot (15 samples) inference technique for classification tasks. Our approach consistently improves traditional fine-tuning using only a handful of labeled samples in target locales.
• Score: 2.578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Even though large pre-trained multilingual models (e.g. mBERT, XLM-R) have led to significant performance gains on a wide range of cross-lingual NLP tasks, success on many downstream tasks still relies on the availability of sufficient annotated data. Traditional fine-tuning of pre-trained models using only a few target samples can cause over-fitting. This can be quite limiting as most languages in the world are under-resourced. In this work, we investigate cross-lingual adaptation using a simple nearest neighbor few-shot (<15 samples) inference technique for classification tasks. We experiment using a total of 16 distinct languages across two NLP tasks- XNLI and PAWS-X. Our approach consistently improves traditional fine-tuning using only a handful of labeled samples in target locales. We also demonstrate its generalization capability across tasks.

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